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Databricks DE-PRO Cheat Sheet: Sharing, Governance, and Federation

April 13, 2026

Databricks DE-PRO cheat sheet for sharing, governance, federation, traps, and final review.

On this page

Use this for last-mile review. DE-PRO usually gets easier when you classify the stem first instead of trying to solve everything at once.

Fast lane picker

If the question is mainly about… Strongest first lane
project packaging, dependencies, tests, or deployment units chapter 1 or chapter 9
file discovery, message-bus input, or append-only vs streaming ingest chapter 2
joins, windows, quarantining, or quality rules chapter 3
data exchange across workspaces or external platforms chapter 4
what signal to inspect first chapter 5 or chapter 9
slow workloads, poor pruning, or bad join plans chapter 6
row visibility, masking, PII protection, or retention chapter 7
discoverability, metadata, or permission inheritance chapter 8
repair, parameter overrides, bundles, or CI/CD chapter 9
partitioning, table shape, medallion fit, or serving design chapter 10

Production answer rules

If you need to choose between… Better DE-PRO instinct
fast once vs safe to rerun safe to rerun
broad reprocessing vs bounded replay bounded replay
manual notebook repair vs auditable job repair auditable repair
bigger cluster vs measured bottleneck analysis measured bottleneck analysis
vague permissions vs specific policy surface specific policy surface

DE-PRO answer sequence

Use this when the stem mixes packaging, quality, observability, governance, or recovery.

    flowchart TD
	  S["Scenario"] --> L["Find the main lane"]
	  L --> P["Package, ingest, transform, govern, observe, or repair?"]
	  P --> F["Choose the narrowest Databricks feature that fits"]
	  F --> R["Check logs, event logs, query profile, or Spark UI"]
	  R --> V["Verify rerun, recovery, or promotion behavior"]

Monitoring and debugging signal map

Need Better first signal
pipeline lifecycle, quality, and declarative run state event log
query-level bottlenecks, joins, skew, or pruning query profile
account or workspace cost, audit, and workload telemetry system tables
low-level stage or task behavior Spark UI
failed-run remediation path Jobs UI, repair state, logs, and parameter overrides

Performance triage table

Symptom Likely cause Better first action
one task runs much longer than peers skew inspect hot keys and shuffle distribution
scans read far too much data weak pruning or bad layout inspect clustering, partitioning, and filter selectivity
too many tiny files write pattern or over-partitioning compact and rethink layout, not just cluster size
repeated high-cost reprocessing weak incremental or replay design tighten boundaries and use targeted reprocessing
poor merge or update performance table layout and file behavior inspect clustering, pruning, and change pattern first

Security, governance, and sharing boundaries

If the question is about… Keep this boundary clear
row filters who can see which records
column masks how sensitive values are transformed or hidden
ACLs or workspace permissions who can access objects or actions
Delta Sharing how live data is exposed to another Databricks deployment or external platform
Lakehouse Federation querying external systems through governed access
Unity Catalog inheritance how permissions flow from higher objects to lower objects

High-confusion pairs

Pair Keep this distinction clear
Lakeflow Declarative Pipelines vs Lakeflow Jobs declarative pipeline logic vs orchestration and run control
checkpoint vs watermark recoverability state vs lateness boundary
event log vs system tables pipeline lifecycle record vs broader platform telemetry
Delta Sharing vs Lakehouse Federation governed data exchange vs governed access to external source systems
row filter vs column mask hide rows vs transform or hide values
repair run vs retry targeted rerun after diagnosis vs automatic repeat attempt
liquid clustering vs partitioning flexible layout strategy vs hard physical split
Databricks Asset Bundles vs Git folders deployment package and targets vs workspace source integration

Last 15-minute recheck

Recheck this Because the miss often hides here
package structure, dependencies, and target config deployment questions break here first
append-only vs streaming ingest boundary ingestion stems often hinge on this choice
expectations, quarantine, and bad-data visibility quality questions reward explicit handling
event logs, system tables, and query profile observability questions punish guessing
liquid clustering, pruning, and shuffle evidence performance questions punish “add compute” reflexes
row filters, masks, sharing mode, and inheritance governance questions reward precise boundaries

One-sentence memory hooks

  • If replay safety matters, think idempotent boundary + targeted rerun, not “reprocess everything.”
  • If the workload is slow, think signal first, not “bigger cluster first.”
  • If the question mentions data exchange, separate sharing from federation.
  • If the question mentions PII, separate masking, filtering, anonymization, and retention.
  • If the question mentions deployment, think bundle targets, environment config, and auditable promotion.
Revised on Sunday, May 10, 2026
Study Plan
Sample Questions